{
  "pdf": "suppression-contrast-secret-elicitation.pdf",
  "title": "SUPPRESSION-CONTRAST TOKENS: EVALUATING RE-VERSE LAYER-CONTRAST FOR SECRET ELICITATION FARS Analemma",
  "elapsed": 73.6,
  "runs_mode": 1,
  "valid_runs": 1,
  "avg_score": 2.5,
  "scores": [
    2.5
  ],
  "score_std": 0,
  "final_verdict": "Strong Reject",
  "final_confidence": 0.6,
  "conference_scores": {
    "soundness": 2.8,
    "presentation": 2.5,
    "contribution": 1.5,
    "overall_rating": 2.5,
    "confidence": 3
  },
  "strengths": [
    "Pre-registered success criteria demonstrate exceptional scientific rigor. The authors committed to 4 specific criteria (Section 4.2, Table 3) before running experiments, including both improvement thresholds and a negative control, which prevents post-hoc rationalization of marginal results. This is commendable and rare in ML research.",
    "The DoLa-direction negative control is well-designed and informative. The near-zero performance of the DoLa-direction baseline (0.20% TR@5 vs 4.33% for logit lens, Table 1) confirms that the suppression direction (mid-minus-final) carries meaningful signal, validating Criterion 3. This provides a clean ablation that isolates the effect of contrast direction.",
    "Honest reporting of negative results is valuable for the community. The paper transparently reports that SCT fails 3 of 4 pre-registered criteria (Table 3), that the suppression premise is weakly supported (~9.3% vs 30% threshold), and that SCT does not generalize to binary-attribute secrets (Table 2). This prevents other researchers from pursuing the same failed hypothesis without awareness of its limitations."
  ],
  "weaknesses": [
    "The core contribution is extremely incremental: SCT is simply reversing the sign of DoLa's layer contrast (Equation 1: score = mid-logprob minus final-logprob). This is a one-line change from DoLa (log pN - log pL → log pL - log pN), with no new algorithmic insight beyond the 'suppression hypothesis' which the paper's own experiments show is weakly supported (~9.3%, Table 3). The contribution is essentially a negative result on a trivially constructed variant.",
    "The absolute performance levels are negligible across all methods, undermining the practical relevance. Even the best result (SCT on Taboo: 5.33% TR@5, Table 1) means the secret is recovered in only ~1 in 20 attempts. Logit lens achieves 4.33%. These rates are so low that none of these methods are useful for actual safety auditing, and the relative improvement (+23.1%) is misleading given the tiny absolute gain (+1.0pp).",
    "The paper is generated by an automated research system (explicitly stated in the abstract), which raises concerns about depth of scientific reasoning. The experimental design, while rigorous in structure (pre-registration), lacks exploratory analysis that a human researcher would conduct—e.g., no analysis of which examples DO show the suppression pattern, no visualization of layer-wise probability trajectories for the secret token, no investigation of why the premise fails for ~90.7% of examples. These would be critical for understanding whether the hypothesis is wrong or just partially correct."
  ],
  "must_fix_items": [
    "Provide per-example analysis of the ~9.3% of cases where the suppression premise holds: what distinguishes these from the ~90.7% where it fails? Without this, the paper cannot distinguish 'hypothesis is wrong' from 'hypothesis is correct but premise is too restrictive.'",
    "Report confidence intervals for all results in Tables 1 and 2, not just mention they 'overlap' in Table 3. The reader needs to see the actual CIs to assess statistical significance independently.",
    "The top-200 token extraction ceiling (Section 4.6) is a major confound—report what SCT achieves on the subset where the secret IS in the top-200, to isolate the scoring method's contribution from the candidate generation bottleneck."
  ],
  "runs": [
    {
      "run": 1,
      "score": 2.5,
      "verdict": "Strong Reject",
      "confidence": 0.6,
      "strengths": [
        "Pre-registered success criteria demonstrate exceptional scientific rigor. The authors committed to 4 specific criteria (Section 4.2, Table 3) before running experiments, including both improvement thresholds and a negative control, which prevents post-hoc rationalization of marginal results. This is commendable and rare in ML research.",
        "The DoLa-direction negative control is well-designed and informative. The near-zero performance of the DoLa-direction baseline (0.20% TR@5 vs 4.33% for logit lens, Table 1) confirms that the suppression direction (mid-minus-final) carries meaningful signal, validating Criterion 3. This provides a clean ablation that isolates the effect of contrast direction.",
        "Honest reporting of negative results is valuable for the community. The paper transparently reports that SCT fails 3 of 4 pre-registered criteria (Table 3), that the suppression premise is weakly supported (~9.3% vs 30% threshold), and that SCT does not generalize to binary-attribute secrets (Table 2). This prevents other researchers from pursuing the same failed hypothesis without awareness of its limitations."
      ],
      "weaknesses": [
        "The core contribution is extremely incremental: SCT is simply reversing the sign of DoLa's layer contrast (Equation 1: score = mid-logprob minus final-logprob). This is a one-line change from DoLa (log pN - log pL → log pL - log pN), with no new algorithmic insight beyond the 'suppression hypothesis' which the paper's own experiments show is weakly supported (~9.3%, Table 3). The contribution is essentially a negative result on a trivially constructed variant.",
        "The absolute performance levels are negligible across all methods, undermining the practical relevance. Even the best result (SCT on Taboo: 5.33% TR@5, Table 1) means the secret is recovered in only ~1 in 20 attempts. Logit lens achieves 4.33%. These rates are so low that none of these methods are useful for actual safety auditing, and the relative improvement (+23.1%) is misleading given the tiny absolute gain (+1.0pp).",
        "The paper is generated by an automated research system (explicitly stated in the abstract), which raises concerns about depth of scientific reasoning. The experimental design, while rigorous in structure (pre-registration), lacks exploratory analysis that a human researcher would conduct—e.g., no analysis of which examples DO show the suppression pattern, no visualization of layer-wise probability trajectories for the secret token, no investigation of why the premise fails for ~90.7% of examples. These would be critical for understanding whether the hypothesis is wrong or just partially correct."
      ],
      "must_fix_items": [
        "Provide per-example analysis of the ~9.3% of cases where the suppression premise holds: what distinguishes these from the ~90.7% where it fails? Without this, the paper cannot distinguish 'hypothesis is wrong' from 'hypothesis is correct but premise is too restrictive.'",
        "Report confidence intervals for all results in Tables 1 and 2, not just mention they 'overlap' in Table 3. The reader needs to see the actual CIs to assess statistical significance independently.",
        "The top-200 token extraction ceiling (Section 4.6) is a major confound—report what SCT achieves on the subset where the secret IS in the top-200, to isolate the scoring method's contribution from the candidate generation bottleneck."
      ],
      "conference_scores": {
        "soundness": 2.8,
        "presentation": 2.5,
        "contribution": 1.5,
        "overall_rating": 2.5,
        "confidence": 3
      }
    }
  ]
}